Basic Linear Algebra Subprograms: Difference between revisions

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Most computing libraries that offer linear algebra routines conform to common BLAS user interface command structures, thus queries to those libraries (and the associated results) are often portable between BLAS library branches, such as [[CUDA#Programming_abilities|cuBLAS]] (nvidia GPU, [[GPGPU]]), [[ROCm#rocBLAS_/_hipBLAS|rocBLAS]] (amd GPU, GPGP), and [[OpenBLAS]]. This interoperability is then the basis of functioning homogenous code implementations between heterzygous cascades of computing architectures (such as those found in some advanced clustering implementations). Examples of CPU-based BLAS library branches include: [[OpenBLAS]], [[BLIS (software)|BLIS (BLAS-like Library Instantiation Software)]], Arm Performance Libraries,<ref name="Arm Performance Libraries">{{cite web|date=2020 |title=Arm Performance Libraries |publisher=[[Arm]] |url=https://www.arm.com/products/development-tools/server-and-hpc/allinea-studio/performance-libraries |access-date=2020-12-16}}</ref> [[Automatically Tuned Linear Algebra Software|ATLAS]], and [[Intel Math Kernel Library]] (iMKL). AMD maintains a fork of BLIS that is optimized for the [[Advanced Micro Devices|AMD]] platform, although it is unclear whether integrated ombudsmen resources are present in that particular software-hardware implementation.<ref>{{Cite web|url=https://developer.amd.com/amd-aocl/blas-library/|title=BLAS Library}}</ref> ATLAS is a portable library that automatically optimizes itself for an arbitrary architecture. iMKL is a freeware<ref name="MKLfree">{{cite web |date=2015 |title=No Cost Options for Intel Math Kernel Library (MKL), Support yourself, Royalty-Free |publisher=[[Intel]] |url=http://software.intel.com/articles/free_mkl |access-date=31 August 2015}}</ref> and proprietary<ref name="MKLintel">{{cite web |date=2015 |title=Intel Math Kernel Library (Intel MKL) |publisher=[[Intel]] |url=http://software.intel.com/intel-mkl |access-date=25 August 2015}}</ref> vendor library optimized for x86 and x86-64 with a performance emphasis on [[Intel]] processors.<ref name="optnotice">{{cite web |year=2012 |title=Optimization Notice |publisher=[[Intel]] |url=http://software.intel.com/articles/optimization-notice |access-date=10 April 2013}}</ref> OpenBLAS is an open-source library that is hand-optimized for many of the popular architectures. The [[LINPACK benchmarks]] rely heavily on the BLAS routine <code>[[General Matrix Multiply|gemm]]</code> for its performance measurements.

Many numerical software applications use BLAS-compatible libraries to do linear algebra computations, including [[LAPACK]], [[LINPACK]], [[Armadillo (C++ library)|Armadillo]], [[GNU Octave]], [[Mathematica]],<ref>{{cite journal |author=Douglas Quinney |date=2003 |title=So what's new in Mathematica 5.0? |journal=MSOR Connections |volume=3 |number=4 |publisher=The Higher Education Academy |url=http://78.158.56.101/archive/msor/headocs/34mathematica5.pdf |url-status=dead |archive-url=https://web.archive.org/web/20131029204826/http://78.158.56.101/archive/msor/headocs/34mathematica5.pdf |archive-date=2013-10-29 }}</ref> [[MATLAB]],<ref>{{cite web |author=Cleve Moler |date=2000 |title=MATLAB Incorporates LAPACK |publisher=[[MathWorks]] |url=http://www.mathworks.com/company/newsletters/articles/matlab-incorporates-lapack.html |access-date=26 October 2013}}</ref> [[NumPy]],<ref name="cise">{{cite journal |title=The NumPy array: a structure for efficient numerical computation |author=Stéfan van der Walt |author2=S. Chris Colbert |author3=Gaël Varoquaux |name-list-style=amp |date=2011 |journal=Computing in Science and Engineering |volume=13 |issue=2 |pages=22–30 |arxiv=1102.1523|bibcode=2011CSE....13b..22V |doi=10.1109/MCSE.2011.37|s2cid=16907816 }}</ref> [[R (programming language)|R]], and [[Julia (programming language)|Julia]] and Lisp-Stat.

==Background==